Generate knowledge base from very high spatial resolution satellite image using robust classification rules and genetic programming
Rida Azmi, Hicham Amar, Abderrahim Norelyaqine · 2020
Object based image analysis techniques give accurate results when a good knowledge base is extracted from remote sensing imagery. Data mining algorithms and especially evolutionary process can extract useful knowledge that can be used in different fields. In this paper, object-oriented classification was used, more particularly object-based image analysis approach (OOIA) to classify a large feature space composed of a very high spatial resolution satellite image (VHR). Genetic programming (GP) concept was applied to extract classification rules with an induction form. Comparison of the performance of three GP algorithms (Bojarczuc_GP, Falco_GP and Tan_GP) was mad using JCLEC Framework. Results showed two main conclusions. 1) testing and evaluation of the generated rules allow us to discover that GP algorithms can classify and extract useful knowledge from VHR satellite data. 2) evaluation of the performance of the three Genetic programming models demonstrates that the Bojarczuk model is efficient on accuracy classification than the Falco and Tan models.